Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
This addresses safety-critical failures in detecting traffic participants like pedestrians for self-driving systems, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of 3D object detectors for self-driving cars failing to generalize across diverse environments by proposing an unsupervised domain adaptation method using unlabeled repeated traversals, achieving up to a 20-point performance gain in detection.
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utilizes unlabeled repeated traversals of multiple locations to adapt object detectors to new driving environments. By incorporating statistics computed from repeated LiDAR scans, we guide the adaptation process effectively. Our approach enhances LiDAR-based detection models using spatial quantized historical features and introduces a lightweight regression head to leverage the statistics for feature regularization. Additionally, we leverage the statistics for a novel self-training process to stabilize the training. The framework is detector model-agnostic and experiments on real-world datasets demonstrate significant improvements, achieving up to a 20-point performance gain, especially in detecting pedestrians and distant objects. Code is available at https://github.com/zhangtravis/Hist-DA.